Added all missing asserts for the manifold_regularization code. Also

removed unnecessary requirement that distances be non-negative for the
graph creation tools.  All that really matters is that edge weights
are non-negative.  A user can use that distance float for whatever they
want as long as the above remains true.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403632

dlib/manifold_regularization/graph_creation.h

@@ -62,6 +62,18 @@ namespace dlib
         std::vector<sample_pair, alloc>& out
     )
     {
+        // make sure requires clause is not broken
+        DLIB_ASSERT(samples.size() > 1 &&
+                    0 < percent && percent <= 1 &&
+                    num > 0,
+            "\t void find_percent_shortest_edges_randomly()"
+            << "\n\t Invalid inputs were given to this function."
+            << "\n\t samples.size(): " << samples.size()
+            << "\n\t percent:        " << percent 
+            << "\n\t num:            " << num 
+            );
+
+
         std::vector<sample_pair, alloc> edges;
         edges.reserve(num);
 
@@ -116,6 +128,14 @@ namespace dlib
         std::vector<sample_pair, alloc>& out
     )
     {
+        // make sure requires clause is not broken
+        DLIB_ASSERT(samples.size() > k && k > 0,
+            "\t void find_k_nearest_neighbors()"
+            << "\n\t Invalid inputs were given to this function."
+            << "\n\t samples.size(): " << samples.size()
+            << "\n\t k:              " << k 
+            );
+
         using namespace impl;
         std::vector<sample_pair> edges;
 

dlib/manifold_regularization/graph_creation_abstract.h

@@ -33,7 +33,7 @@ namespace dlib
             - num > 0
             - random_seed must be convertible to a string by dlib::cast_to_string()
             - dist_funct(samples[i], samples[j]) must be a valid expression that evaluates
-              to a floating point number >= 0
+              to a floating point number 
         ensures
             - This function randomly samples the space of pairs of integers between
               0 and samples.size()-1 inclusive.  For each of these pairs, (i,j), a
@@ -68,7 +68,7 @@ namespace dlib
             - samples.size() > k
             - k > 0
             - dist_funct(samples[i], samples[j]) must be a valid expression that evaluates
-              to a floating point number >= 0
+              to a floating point number 
         ensures
             - #out == a set of sample_pair objects that represent all the k nearest
               neighbors in samples according to the given distance function dist_funct.

dlib/manifold_regularization/linear_manifold_regularizer.h

@@ -136,6 +136,14 @@ namespace dlib
                 for (unsigned long i = 0; i < edges.size(); ++i)
                 {
                     const float weight = weight_funct(edges[i]);
+
+                    // make sure requires clause is not broken
+                    DLIB_ASSERT(weight >= 0,
+                        "\t void linear_manifold_regularizer::build()"
+                        << "\n\t You supplied a weight_funct() that generated a negative weight."
+                        << "\n\t weight: " << weight 
+                        );
+
                     *mutable_blocks[edges[i].index1()]++ = neighbor(edges[i].index2(), weight);
                     *mutable_blocks[edges[i].index2()]++ = neighbor(edges[i].index1(), weight);
                 }
@@ -192,6 +200,19 @@ namespace dlib
             const weight_function_type& weight_funct
         )
         {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(edges.size() > 0 &&
+                        contains_duplicate_pairs(edges) == false &&
+                        max_index_value_plus_one(edges) <= samples.size(),
+                "\t void linear_manifold_regularizer::build()"
+                << "\n\t Invalid inputs were given to this function."
+                << "\n\t edges.size():                    " << edges.size()
+                << "\n\t samples.size():                  " << samples.size()
+                << "\n\t contains_duplicate_pairs(edges): " << contains_duplicate_pairs(edges) 
+                << "\n\t max_index_value_plus_one(edges): " << max_index_value_plus_one(edges) 
+                );
+
+
             impl::undirected_adjacency_list graph;
             graph.build(edges, weight_funct);
 
@@ -207,6 +228,13 @@ namespace dlib
             scalar_type intrinsic_regularization_strength
         ) const
         {
+            // make sure requires clause is not broken
+            DLIB_ASSERT(intrinsic_regularization_strength >= 0,
+                "\t matrix linear_manifold_regularizer::get_transformation_matrix()"
+                << "\n\t This value must not be negative"
+                << "\n\t intrinsic_regularization_strength: " << intrinsic_regularization_strength 
+                );
+
             if (dimensionality() == 0)
                 return general_matrix();